Overview

Dataset statistics

Number of variables36
Number of observations9590
Missing cells5069
Missing cells (%)1.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.6 MiB
Average record size in memory288.0 B

Variable types

Numeric19
Categorical17

Alerts

when has a high cardinality: 622 distinct values High cardinality
crystal_type has a high cardinality: 206 distinct values High cardinality
expected_start has a high cardinality: 9070 distinct values High cardinality
start_process has a high cardinality: 9224 distinct values High cardinality
start_subprocess1 has a high cardinality: 9118 distinct values High cardinality
start_critical_subprocess1 has a high cardinality: 6294 distinct values High cardinality
predicted_process_end has a high cardinality: 8963 distinct values High cardinality
process_end has a high cardinality: 9219 distinct values High cardinality
subprocess1_end has a high cardinality: 9113 distinct values High cardinality
reported_on_tower has a high cardinality: 8883 distinct values High cardinality
opened has a high cardinality: 8914 distinct values High cardinality
df_index is highly correlated with targetHigh correlation
expected_factor_x is highly correlated with expected_final_factor_x and 3 other fieldsHigh correlation
previous_factor_x is highly correlated with first_factor_x and 2 other fieldsHigh correlation
first_factor_x is highly correlated with previous_factor_x and 3 other fieldsHigh correlation
expected_final_factor_x is highly correlated with expected_factor_x and 2 other fieldsHigh correlation
final_factor_x is highly correlated with expected_factor_x and 1 other fieldsHigh correlation
previous_adamantium is highly correlated with etherium_before_start and 2 other fieldsHigh correlation
Unnamed_17 is highly correlated with previous_factor_x and 3 other fieldsHigh correlation
etherium_before_start is highly correlated with expected_factor_x and 3 other fieldsHigh correlation
chemical_x is highly correlated with argon and 2 other fieldsHigh correlation
argon is highly correlated with previous_factor_x and 5 other fieldsHigh correlation
pure_seastone is highly correlated with previous_adamantium and 4 other fieldsHigh correlation
crystal_supergroup is highly correlated with expected_factor_x and 5 other fieldsHigh correlation
target is highly correlated with df_indexHigh correlation
place is highly correlated with groupsHigh correlation
raw_kryptonite is highly correlated with first_factor_x and 1 other fieldsHigh correlation
Cycle is highly correlated with groupsHigh correlation
groups is highly correlated with place and 2 other fieldsHigh correlation
start_subprocess1 has 113 (1.2%) missing values Missing
start_critical_subprocess1 has 3068 (32.0%) missing values Missing
predicted_process_end has 267 (2.8%) missing values Missing
subprocess1_end has 120 (1.3%) missing values Missing
reported_on_tower has 203 (2.1%) missing values Missing
opened has 203 (2.1%) missing values Missing
target has 1045 (10.9%) missing values Missing
expected_start is uniformly distributed Uniform
start_process is uniformly distributed Uniform
start_subprocess1 is uniformly distributed Uniform
start_critical_subprocess1 is uniformly distributed Uniform
predicted_process_end is uniformly distributed Uniform
process_end is uniformly distributed Uniform
subprocess1_end is uniformly distributed Uniform
reported_on_tower is uniformly distributed Uniform
opened is uniformly distributed Uniform
human_measure has unique values Unique
crystal_weight has unique values Unique
expected_factor_x has unique values Unique
previous_factor_x has unique values Unique
first_factor_x has unique values Unique
expected_final_factor_x has unique values Unique
final_factor_x has unique values Unique
previous_adamantium has unique values Unique
Unnamed_17 has unique values Unique
raw_kryptonite has unique values Unique
argon has unique values Unique
pure_seastone has unique values Unique
groups has 154 (1.6%) zeros Zeros

Reproduction

Analysis started2022-09-13 12:22:13.463107
Analysis finished2022-09-13 12:23:15.146295
Duration1 minute and 1.68 second
Software versionpandas-profiling v3.3.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION

Distinct171
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.96173097
Minimum0
Maximum170
Zeros75
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size75.0 KiB
2022-09-13T13:23:15.232296image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q131
median63
Q396
95-th percentile130
Maximum170
Range170
Interquartile range (IQR)65

Descriptive statistics

Standard deviation39.28355005
Coefficient of variation (CV)0.604718339
Kurtosis-0.99322646
Mean64.96173097
Median Absolute Deviation (MAD)32
Skewness0.1683614486
Sum622983
Variance1543.197305
MonotonicityNot monotonic
2022-09-13T13:23:15.393502image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1476
 
0.8%
075
 
0.8%
5475
 
0.8%
5275
 
0.8%
5175
 
0.8%
5075
 
0.8%
4975
 
0.8%
4875
 
0.8%
4775
 
0.8%
4675
 
0.8%
Other values (161)8839
92.2%
ValueCountFrequency (%)
075
0.8%
175
0.8%
275
0.8%
375
0.8%
475
0.8%
575
0.8%
675
0.8%
775
0.8%
875
0.8%
975
0.8%
ValueCountFrequency (%)
1701
< 0.1%
1691
< 0.1%
1681
< 0.1%
1671
< 0.1%
1662
< 0.1%
1652
< 0.1%
1642
< 0.1%
1632
< 0.1%
1622
< 0.1%
1612
< 0.1%

when
Categorical

HIGH CARDINALITY

Distinct622
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Memory size75.0 KiB
7/20/2020
 
44
7/29/2020
 
40
7/30/2020
 
39
7/28/2020
 
37
7/14/2020
 
36
Other values (617)
9394 

Length

Max length10
Median length9
Mean length8.920959333
Min length8

Characters and Unicode

Total characters85552
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)0.1%

Sample

1st row9/7/2020
2nd row9/7/2020
3rd row9/7/2020
4th row9/7/2020
5th row9/7/2020

Common Values

ValueCountFrequency (%)
7/20/202044
 
0.5%
7/29/202040
 
0.4%
7/30/202039
 
0.4%
7/28/202037
 
0.4%
7/14/202036
 
0.4%
7/16/202036
 
0.4%
2/8/202033
 
0.3%
11/7/202032
 
0.3%
7/18/202032
 
0.3%
12/7/202031
 
0.3%
Other values (612)9230
96.2%

Length

2022-09-13T13:23:15.548499image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
7/20/202044
 
0.5%
7/29/202040
 
0.4%
7/30/202039
 
0.4%
7/28/202037
 
0.4%
7/14/202036
 
0.4%
7/16/202036
 
0.4%
2/8/202033
 
0.3%
11/7/202032
 
0.3%
7/18/202032
 
0.3%
6/8/202031
 
0.3%
Other values (612)9230
96.2%

Most occurring characters

ValueCountFrequency (%)
220726
24.2%
/19180
22.4%
016025
18.7%
113309
15.6%
95369
 
6.3%
32216
 
2.6%
72204
 
2.6%
61906
 
2.2%
81719
 
2.0%
51480
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number66372
77.6%
Other Punctuation19180
 
22.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
220726
31.2%
016025
24.1%
113309
20.1%
95369
 
8.1%
32216
 
3.3%
72204
 
3.3%
61906
 
2.9%
81719
 
2.6%
51480
 
2.2%
41418
 
2.1%
Other Punctuation
ValueCountFrequency (%)
/19180
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common85552
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
220726
24.2%
/19180
22.4%
016025
18.7%
113309
15.6%
95369
 
6.3%
32216
 
2.6%
72204
 
2.6%
61906
 
2.2%
81719
 
2.0%
51480
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII85552
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
220726
24.2%
/19180
22.4%
016025
18.7%
113309
15.6%
95369
 
6.3%
32216
 
2.6%
72204
 
2.6%
61906
 
2.2%
81719
 
2.0%
51480
 
1.7%

super_hero_group
Categorical

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size75.0 KiB
B
2344 
D
2104 
A
2049 
C
2005 
G
378 
Other values (3)
710 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9590
Distinct characters8
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowD
2nd rowD
3rd rowD
4th rowD
5th rowD

Common Values

ValueCountFrequency (%)
B2344
24.4%
D2104
21.9%
A2049
21.4%
C2005
20.9%
G378
 
3.9%
W369
 
3.8%
Y340
 
3.5%
1
 
< 0.1%

Length

2022-09-13T13:23:15.671259image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-13T13:23:15.806865image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
b2344
24.4%
d2104
21.9%
a2049
21.4%
c2005
20.9%
g378
 
3.9%
w369
 
3.8%
y340
 
3.5%
1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
B2344
24.4%
D2104
21.9%
A2049
21.4%
C2005
20.9%
G378
 
3.9%
W369
 
3.8%
Y340
 
3.5%
1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter9589
> 99.9%
Currency Symbol1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B2344
24.4%
D2104
21.9%
A2049
21.4%
C2005
20.9%
G378
 
3.9%
W369
 
3.8%
Y340
 
3.5%
Currency Symbol
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin9589
> 99.9%
Common1
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
B2344
24.4%
D2104
21.9%
A2049
21.4%
C2005
20.9%
G378
 
3.9%
W369
 
3.8%
Y340
 
3.5%
Common
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII9589
> 99.9%
Currency Symbols1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B2344
24.4%
D2104
21.9%
A2049
21.4%
C2005
20.9%
G378
 
3.9%
W369
 
3.8%
Y340
 
3.5%
Currency Symbols
ValueCountFrequency (%)
1
100.0%

tracking
Real number (ℝ≥0)

Distinct8985
Distinct (%)93.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean286933.7414
Minimum9921
Maximum631531
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size75.0 KiB
2022-09-13T13:23:15.955863image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum9921
5-th percentile18095.5
Q160233.5
median119261
Q3559466
95-th percentile615573
Maximum631531
Range621610
Interquartile range (IQR)499232.5

Descriptive statistics

Standard deviation249810.6759
Coefficient of variation (CV)0.8706214707
Kurtosis-1.861505853
Mean286933.7414
Median Absolute Deviation (MAD)98785
Skewness0.2444331688
Sum2751694580
Variance6.240537381 × 1010
MonotonicityNot monotonic
2022-09-13T13:23:16.108872image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1311014
 
< 0.1%
5464214
 
< 0.1%
666014
 
< 0.1%
894814
 
< 0.1%
6174213
 
< 0.1%
6221913
 
< 0.1%
5868213
 
< 0.1%
1310913
 
< 0.1%
5277313
 
< 0.1%
5521713
 
< 0.1%
Other values (8975)9556
99.6%
ValueCountFrequency (%)
99211
< 0.1%
99411
< 0.1%
99611
< 0.1%
99811
< 0.1%
100011
< 0.1%
100111
< 0.1%
100811
< 0.1%
101011
< 0.1%
101211
< 0.1%
101511
< 0.1%
ValueCountFrequency (%)
6315311
< 0.1%
6314911
< 0.1%
6314711
< 0.1%
6314311
< 0.1%
6314111
< 0.1%
6313311
< 0.1%
6313111
< 0.1%
6312111
< 0.1%
6312011
< 0.1%
6308812
< 0.1%

place
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size75.0 KiB
1
5044 
2
4546 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9590
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
15044
52.6%
24546
47.4%

Length

2022-09-13T13:23:16.258865image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-13T13:23:16.615343image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
15044
52.6%
24546
47.4%

Most occurring characters

ValueCountFrequency (%)
15044
52.6%
24546
47.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number9590
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
15044
52.6%
24546
47.4%

Most occurring scripts

ValueCountFrequency (%)
Common9590
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
15044
52.6%
24546
47.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII9590
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
15044
52.6%
24546
47.4%

tracking_times
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size75.0 KiB
1
9278 
2
 
279
3
 
29
4
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9590
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
19278
96.7%
2279
 
2.9%
329
 
0.3%
44
 
< 0.1%

Length

2022-09-13T13:23:16.723294image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-13T13:23:16.847299image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
19278
96.7%
2279
 
2.9%
329
 
0.3%
44
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
19278
96.7%
2279
 
2.9%
329
 
0.3%
44
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number9590
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
19278
96.7%
2279
 
2.9%
329
 
0.3%
44
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common9590
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
19278
96.7%
2279
 
2.9%
329
 
0.3%
44
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII9590
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
19278
96.7%
2279
 
2.9%
329
 
0.3%
44
 
< 0.1%

crystal_type
Categorical

HIGH CARDINALITY

Distinct206
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size75.0 KiB
group 67
 
534
group 5
 
479
group 203
 
472
group 196
 
440
group 35
 
401
Other values (201)
7264 

Length

Max length9
Median length9
Mean length8.388529718
Min length7

Characters and Unicode

Total characters80446
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)0.1%

Sample

1st rowgroup 27
2nd rowgroup 56
3rd rowgroup 56
4th rowgroup 56
5th rowgroup 27

Common Values

ValueCountFrequency (%)
group 67534
 
5.6%
group 5479
 
5.0%
group 203472
 
4.9%
group 196440
 
4.6%
group 35401
 
4.2%
group 184306
 
3.2%
group 112259
 
2.7%
group 197243
 
2.5%
group 190203
 
2.1%
group 70195
 
2.0%
Other values (196)6058
63.2%

Length

2022-09-13T13:23:16.961313image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
group9590
50.0%
67534
 
2.8%
5479
 
2.5%
203472
 
2.5%
196440
 
2.3%
35401
 
2.1%
184306
 
1.6%
112259
 
1.4%
197243
 
1.3%
190203
 
1.1%
Other values (197)6253
32.6%

Most occurring characters

ValueCountFrequency (%)
g9590
11.9%
r9590
11.9%
o9590
11.9%
u9590
11.9%
p9590
11.9%
9590
11.9%
15420
6.7%
22476
 
3.1%
02206
 
2.7%
61987
 
2.5%
Other values (6)10817
13.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter47950
59.6%
Decimal Number22906
28.5%
Space Separator9590
 
11.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
15420
23.7%
22476
10.8%
02206
9.6%
61987
 
8.7%
91980
 
8.6%
31972
 
8.6%
71970
 
8.6%
51822
 
8.0%
81565
 
6.8%
41508
 
6.6%
Lowercase Letter
ValueCountFrequency (%)
g9590
20.0%
r9590
20.0%
o9590
20.0%
u9590
20.0%
p9590
20.0%
Space Separator
ValueCountFrequency (%)
9590
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin47950
59.6%
Common32496
40.4%

Most frequent character per script

Common
ValueCountFrequency (%)
9590
29.5%
15420
16.7%
22476
 
7.6%
02206
 
6.8%
61987
 
6.1%
91980
 
6.1%
31972
 
6.1%
71970
 
6.1%
51822
 
5.6%
81565
 
4.8%
Latin
ValueCountFrequency (%)
g9590
20.0%
r9590
20.0%
o9590
20.0%
u9590
20.0%
p9590
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII80446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
g9590
11.9%
r9590
11.9%
o9590
11.9%
u9590
11.9%
p9590
11.9%
9590
11.9%
15420
6.7%
22476
 
3.1%
02206
 
2.7%
61987
 
2.5%
Other values (6)10817
13.4%

Unnamed_7
Real number (ℝ≥0)

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.29332638
Minimum1
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size75.0 KiB
2022-09-13T13:23:17.090314image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q17
median11
Q316
95-th percentile20
Maximum20
Range19
Interquartile range (IQR)9

Descriptive statistics

Standard deviation5.467924968
Coefficient of variation (CV)0.484173111
Kurtosis-0.8268711942
Mean11.29332638
Median Absolute Deviation (MAD)4
Skewness-0.262181665
Sum108303
Variance29.89820345
MonotonicityNot monotonic
2022-09-13T13:23:17.215318image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
13675
 
7.0%
16663
 
6.9%
2632
 
6.6%
7630
 
6.6%
11626
 
6.5%
9596
 
6.2%
6591
 
6.2%
10591
 
6.2%
1585
 
6.1%
8553
 
5.8%
Other values (7)3448
36.0%
ValueCountFrequency (%)
1585
6.1%
2632
6.6%
6591
6.2%
7630
6.6%
8553
5.8%
9596
6.2%
10591
6.2%
11626
6.5%
12537
5.6%
13675
7.0%
ValueCountFrequency (%)
20548
5.7%
19387
4.0%
18540
5.6%
17503
5.2%
16663
6.9%
15441
4.6%
14492
5.1%
13675
7.0%
12537
5.6%
11626
6.5%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size75.0 KiB
3
4180 
4
3584 
2
976 
1
850 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9590
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row4
3rd row4
4th row3
5th row3

Common Values

ValueCountFrequency (%)
34180
43.6%
43584
37.4%
2976
 
10.2%
1850
 
8.9%

Length

2022-09-13T13:23:17.344316image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-13T13:23:17.470315image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
34180
43.6%
43584
37.4%
2976
 
10.2%
1850
 
8.9%

Most occurring characters

ValueCountFrequency (%)
34180
43.6%
43584
37.4%
2976
 
10.2%
1850
 
8.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number9590
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
34180
43.6%
43584
37.4%
2976
 
10.2%
1850
 
8.9%

Most occurring scripts

ValueCountFrequency (%)
Common9590
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
34180
43.6%
43584
37.4%
2976
 
10.2%
1850
 
8.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII9590
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
34180
43.6%
43584
37.4%
2976
 
10.2%
1850
 
8.9%

human_measure
Real number (ℝ)

UNIQUE

Distinct9590
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0
Minimum-3.458910737
Maximum3.458910737
Zeros0
Zeros (%)0.0%
Negative4795
Negative (%)50.0%
Memory size75.0 KiB
2022-09-13T13:23:17.600978image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-3.458910737
5-th percentile-1.163087301
Q1-0.4769362807
median0
Q30.4769362807
95-th percentile1.163087301
Maximum3.458910737
Range6.917821474
Interquartile range (IQR)0.9538725615

Descriptive statistics

Standard deviation0.7082351688
Coefficient of variation (CV)nan
Kurtosis0.06957175341
Mean0
Median Absolute Deviation (MAD)0.476994291
Skewness-8.345166235 × 10-18
Sum1.072475442 × 10-13
Variance0.5015970543
MonotonicityNot monotonic
2022-09-13T13:23:17.741946image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.0468918971
 
< 0.1%
0.4842133021
 
< 0.1%
-0.1539806041
 
< 0.1%
-0.6855968431
 
< 0.1%
-0.2683164241
 
< 0.1%
-0.2687137481
 
< 0.1%
0.5627581951
 
< 0.1%
-0.5126507961
 
< 0.1%
0.476530261
 
< 0.1%
-0.160422511
 
< 0.1%
Other values (9580)9580
99.9%
ValueCountFrequency (%)
-3.4589107371
< 0.1%
-2.6222353681
< 0.1%
-2.4953746651
< 0.1%
-2.4184571121
< 0.1%
-2.3625573651
< 0.1%
-2.318391531
< 0.1%
-2.281756071
< 0.1%
-2.2503797991
< 0.1%
-2.2228925341
< 0.1%
-2.1984023591
< 0.1%
ValueCountFrequency (%)
3.4589107371
< 0.1%
2.6222353681
< 0.1%
2.4953746651
< 0.1%
2.4184571121
< 0.1%
2.3625573651
< 0.1%
2.318391531
< 0.1%
2.281756071
< 0.1%
2.2503797991
< 0.1%
2.2228925341
< 0.1%
2.1984023591
< 0.1%

crystal_weight
Real number (ℝ)

UNIQUE

Distinct9590
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.48184095 × 10-18
Minimum-3.458910737
Maximum3.458910737
Zeros0
Zeros (%)0.0%
Negative4795
Negative (%)50.0%
Memory size75.0 KiB
2022-09-13T13:23:17.907945image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-3.458910737
5-th percentile-1.163087301
Q1-0.4769362807
median0
Q30.4769362807
95-th percentile1.163087301
Maximum3.458910737
Range6.917821474
Interquartile range (IQR)0.9538725615

Descriptive statistics

Standard deviation0.7082351688
Coefficient of variation (CV)4.779427701 × 1017
Kurtosis0.06957175341
Mean1.48184095 × 10-18
Median Absolute Deviation (MAD)0.476994291
Skewness1.251774935 × 10-17
Sum6.383782392 × 10-14
Variance0.5015970543
MonotonicityNot monotonic
2022-09-13T13:23:18.056947image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2560406091
 
< 0.1%
0.4033825641
 
< 0.1%
0.2675220311
 
< 0.1%
0.0335614941
 
< 0.1%
1.2761087341
 
< 0.1%
0.2059234181
 
< 0.1%
0.2667279751
 
< 0.1%
-0.9727004841
 
< 0.1%
-0.4311028211
 
< 0.1%
-1.3906346581
 
< 0.1%
Other values (9580)9580
99.9%
ValueCountFrequency (%)
-3.4589107371
< 0.1%
-2.6222353681
< 0.1%
-2.4953746651
< 0.1%
-2.4184571121
< 0.1%
-2.3625573651
< 0.1%
-2.318391531
< 0.1%
-2.281756071
< 0.1%
-2.2503797991
< 0.1%
-2.2228925341
< 0.1%
-2.1984023591
< 0.1%
ValueCountFrequency (%)
3.4589107371
< 0.1%
2.6222353681
< 0.1%
2.4953746651
< 0.1%
2.4184571121
< 0.1%
2.3625573651
< 0.1%
2.318391531
< 0.1%
2.281756071
< 0.1%
2.2503797991
< 0.1%
2.2228925341
< 0.1%
2.1984023591
< 0.1%

expected_factor_x
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct9590
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.48184095 × 10-18
Minimum-3.458910737
Maximum3.458910737
Zeros0
Zeros (%)0.0%
Negative4795
Negative (%)50.0%
Memory size75.0 KiB
2022-09-13T13:23:18.205966image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-3.458910737
5-th percentile-1.163087301
Q1-0.4769362807
median0
Q30.4769362807
95-th percentile1.163087301
Maximum3.458910737
Range6.917821474
Interquartile range (IQR)0.9538725615

Descriptive statistics

Standard deviation0.7082351688
Coefficient of variation (CV)-4.779427701 × 1017
Kurtosis0.06957175341
Mean-1.48184095 × 10-18
Median Absolute Deviation (MAD)0.476994291
Skewness-8.345166235 × 10-18
Sum-2.087219286 × 10-14
Variance0.5015970543
MonotonicityNot monotonic
2022-09-13T13:23:18.363945image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.483512491
 
< 0.1%
0.6483914161
 
< 0.1%
0.8521097971
 
< 0.1%
-0.24462631
 
< 0.1%
-0.1443414791
 
< 0.1%
0.8082917981
 
< 0.1%
0.8407578271
 
< 0.1%
0.5526668891
 
< 0.1%
-0.0925927151
 
< 0.1%
-0.6261951211
 
< 0.1%
Other values (9580)9580
99.9%
ValueCountFrequency (%)
-3.4589107371
< 0.1%
-2.6222353681
< 0.1%
-2.4953746651
< 0.1%
-2.4184571121
< 0.1%
-2.3625573651
< 0.1%
-2.318391531
< 0.1%
-2.281756071
< 0.1%
-2.2503797991
< 0.1%
-2.2228925341
< 0.1%
-2.1984023591
< 0.1%
ValueCountFrequency (%)
3.4589107371
< 0.1%
2.6222353681
< 0.1%
2.4953746651
< 0.1%
2.4184571121
< 0.1%
2.3625573651
< 0.1%
2.318391531
< 0.1%
2.281756071
< 0.1%
2.2503797991
< 0.1%
2.2228925341
< 0.1%
2.1984023591
< 0.1%

previous_factor_x
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct9590
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0
Minimum-3.458910737
Maximum3.458910737
Zeros0
Zeros (%)0.0%
Negative4795
Negative (%)50.0%
Memory size75.0 KiB
2022-09-13T13:23:18.519979image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-3.458910737
5-th percentile-1.163087301
Q1-0.4769362807
median0
Q30.4769362807
95-th percentile1.163087301
Maximum3.458910737
Range6.917821474
Interquartile range (IQR)0.9538725615

Descriptive statistics

Standard deviation0.7082351688
Coefficient of variation (CV)nan
Kurtosis0.06957175341
Mean0
Median Absolute Deviation (MAD)0.476994291
Skewness-1.669033247 × 10-17
Sum-4.513056595 × 10-14
Variance0.5015970543
MonotonicityNot monotonic
2022-09-13T13:23:18.659946image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1112705411
 
< 0.1%
0.4893672171
 
< 0.1%
-0.1802125351
 
< 0.1%
-0.2078526781
 
< 0.1%
0.2113292591
 
< 0.1%
-0.288892471
 
< 0.1%
0.479550151
 
< 0.1%
0.2746838771
 
< 0.1%
1.26405021
 
< 0.1%
1.0036161811
 
< 0.1%
Other values (9580)9580
99.9%
ValueCountFrequency (%)
-3.4589107371
< 0.1%
-2.6222353681
< 0.1%
-2.4953746651
< 0.1%
-2.4184571121
< 0.1%
-2.3625573651
< 0.1%
-2.318391531
< 0.1%
-2.281756071
< 0.1%
-2.2503797991
< 0.1%
-2.2228925341
< 0.1%
-2.1984023591
< 0.1%
ValueCountFrequency (%)
3.4589107371
< 0.1%
2.6222353681
< 0.1%
2.4953746651
< 0.1%
2.4184571121
< 0.1%
2.3625573651
< 0.1%
2.318391531
< 0.1%
2.281756071
< 0.1%
2.2503797991
< 0.1%
2.2228925341
< 0.1%
2.1984023591
< 0.1%

first_factor_x
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct9590
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-7.409204752 × 10-19
Minimum-3.458910737
Maximum3.458910737
Zeros0
Zeros (%)0.0%
Negative4795
Negative (%)50.0%
Memory size75.0 KiB
2022-09-13T13:23:18.835944image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-3.458910737
5-th percentile-1.163087301
Q1-0.4769362807
median0
Q30.4769362807
95-th percentile1.163087301
Maximum3.458910737
Range6.917821474
Interquartile range (IQR)0.9538725615

Descriptive statistics

Standard deviation0.7082351688
Coefficient of variation (CV)-9.558855403 × 1017
Kurtosis0.06957175341
Mean-7.409204752 × 10-19
Median Absolute Deviation (MAD)0.476994291
Skewness8.345166235 × 10-18
Sum2.664535259 × 10-15
Variance0.5015970543
MonotonicityNot monotonic
2022-09-13T13:23:19.019946image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0970690151
 
< 0.1%
0.3170571861
 
< 0.1%
-0.8333086511
 
< 0.1%
-0.0938979121
 
< 0.1%
0.174108971
 
< 0.1%
-0.6347219261
 
< 0.1%
0.2001447621
 
< 0.1%
0.3217652061
 
< 0.1%
1.4428743511
 
< 0.1%
0.3973072671
 
< 0.1%
Other values (9580)9580
99.9%
ValueCountFrequency (%)
-3.4589107371
< 0.1%
-2.6222353681
< 0.1%
-2.4953746651
< 0.1%
-2.4184571121
< 0.1%
-2.3625573651
< 0.1%
-2.318391531
< 0.1%
-2.281756071
< 0.1%
-2.2503797991
< 0.1%
-2.2228925341
< 0.1%
-2.1984023591
< 0.1%
ValueCountFrequency (%)
3.4589107371
< 0.1%
2.6222353681
< 0.1%
2.4953746651
< 0.1%
2.4184571121
< 0.1%
2.3625573651
< 0.1%
2.318391531
< 0.1%
2.281756071
< 0.1%
2.2503797991
< 0.1%
2.2228925341
< 0.1%
2.1984023591
< 0.1%

expected_final_factor_x
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct9590
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-2.963681901 × 10-18
Minimum-3.458910737
Maximum3.458910737
Zeros0
Zeros (%)0.0%
Negative4795
Negative (%)50.0%
Memory size75.0 KiB
2022-09-13T13:23:19.193958image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-3.458910737
5-th percentile-1.163087301
Q1-0.4769362807
median0
Q30.4769362807
95-th percentile1.163087301
Maximum3.458910737
Range6.917821474
Interquartile range (IQR)0.9538725615

Descriptive statistics

Standard deviation0.7082351688
Coefficient of variation (CV)-2.389713851 × 1017
Kurtosis0.06957175341
Mean-2.963681901 × 10-18
Median Absolute Deviation (MAD)0.476994291
Skewness0
Sum8.92341756 × 10-14
Variance0.5015970543
MonotonicityNot monotonic
2022-09-13T13:23:19.389948image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.0396694961
 
< 0.1%
-0.2699062291
 
< 0.1%
-0.3850291211
 
< 0.1%
-0.4461145241
 
< 0.1%
-0.3091058951
 
< 0.1%
-0.1034171481
 
< 0.1%
1.6082317981
 
< 0.1%
0.7393358421
 
< 0.1%
0.0974422131
 
< 0.1%
-0.2299598831
 
< 0.1%
Other values (9580)9580
99.9%
ValueCountFrequency (%)
-3.4589107371
< 0.1%
-2.6222353681
< 0.1%
-2.4953746651
< 0.1%
-2.4184571121
< 0.1%
-2.3625573651
< 0.1%
-2.318391531
< 0.1%
-2.281756071
< 0.1%
-2.2503797991
< 0.1%
-2.2228925341
< 0.1%
-2.1984023591
< 0.1%
ValueCountFrequency (%)
3.4589107371
< 0.1%
2.6222353681
< 0.1%
2.4953746651
< 0.1%
2.4184571121
< 0.1%
2.3625573651
< 0.1%
2.318391531
< 0.1%
2.281756071
< 0.1%
2.2503797991
< 0.1%
2.2228925341
< 0.1%
2.1984023591
< 0.1%

final_factor_x
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct9590
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.48184095 × 10-18
Minimum-3.458910737
Maximum3.458910737
Zeros0
Zeros (%)0.0%
Negative4795
Negative (%)50.0%
Memory size75.0 KiB
2022-09-13T13:23:19.580965image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-3.458910737
5-th percentile-1.163087301
Q1-0.4769362807
median0
Q30.4769362807
95-th percentile1.163087301
Maximum3.458910737
Range6.917821474
Interquartile range (IQR)0.9538725615

Descriptive statistics

Standard deviation0.7082351688
Coefficient of variation (CV)-4.779427701 × 1017
Kurtosis0.06957175341
Mean-1.48184095 × 10-18
Median Absolute Deviation (MAD)0.476994291
Skewness0
Sum3.025357742 × 10-15
Variance0.5015970543
MonotonicityNot monotonic
2022-09-13T13:23:19.729945image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.2631588661
 
< 0.1%
-0.2718954061
 
< 0.1%
-0.2395303371
 
< 0.1%
-0.4957282471
 
< 0.1%
-0.2961412781
 
< 0.1%
-0.0376331891
 
< 0.1%
1.5668892121
 
< 0.1%
0.7620682521
 
< 0.1%
0.2651408711
 
< 0.1%
0.0531923181
 
< 0.1%
Other values (9580)9580
99.9%
ValueCountFrequency (%)
-3.4589107371
< 0.1%
-2.6222353681
< 0.1%
-2.4953746651
< 0.1%
-2.4184571121
< 0.1%
-2.3625573651
< 0.1%
-2.318391531
< 0.1%
-2.281756071
< 0.1%
-2.2503797991
< 0.1%
-2.2228925341
< 0.1%
-2.1984023591
< 0.1%
ValueCountFrequency (%)
3.4589107371
< 0.1%
2.6222353681
< 0.1%
2.4953746651
< 0.1%
2.4184571121
< 0.1%
2.3625573651
< 0.1%
2.318391531
< 0.1%
2.281756071
< 0.1%
2.2503797991
< 0.1%
2.2228925341
< 0.1%
2.1984023591
< 0.1%

previous_adamantium
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct9590
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-2.963681901 × 10-18
Minimum-3.458910737
Maximum3.458910737
Zeros0
Zeros (%)0.0%
Negative4795
Negative (%)50.0%
Memory size75.0 KiB
2022-09-13T13:23:20.142848image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-3.458910737
5-th percentile-1.163087301
Q1-0.4769362807
median0
Q30.4769362807
95-th percentile1.163087301
Maximum3.458910737
Range6.917821474
Interquartile range (IQR)0.9538725615

Descriptive statistics

Standard deviation0.7082351688
Coefficient of variation (CV)-2.389713851 × 1017
Kurtosis0.06957175341
Mean-2.963681901 × 10-18
Median Absolute Deviation (MAD)0.476994291
Skewness1.669033247 × 10-17
Sum-1.187938636 × 10-14
Variance0.5015970543
MonotonicityNot monotonic
2022-09-13T13:23:20.309847image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1398147931
 
< 0.1%
1.7380134331
 
< 0.1%
0.7263690441
 
< 0.1%
0.5838131671
 
< 0.1%
1.5080028481
 
< 0.1%
1.3830447571
 
< 0.1%
0.3139943061
 
< 0.1%
0.6064663281
 
< 0.1%
0.1704911361
 
< 0.1%
-0.2756807931
 
< 0.1%
Other values (9580)9580
99.9%
ValueCountFrequency (%)
-3.4589107371
< 0.1%
-2.6222353681
< 0.1%
-2.4953746651
< 0.1%
-2.4184571121
< 0.1%
-2.3625573651
< 0.1%
-2.318391531
< 0.1%
-2.281756071
< 0.1%
-2.2503797991
< 0.1%
-2.2228925341
< 0.1%
-2.1984023591
< 0.1%
ValueCountFrequency (%)
3.4589107371
< 0.1%
2.6222353681
< 0.1%
2.4953746651
< 0.1%
2.4184571121
< 0.1%
2.3625573651
< 0.1%
2.318391531
< 0.1%
2.281756071
< 0.1%
2.2503797991
< 0.1%
2.2228925341
< 0.1%
2.1984023591
< 0.1%

Unnamed_17
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct9590
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-7.409204752 × 10-19
Minimum-3.458910737
Maximum3.458910737
Zeros0
Zeros (%)0.0%
Negative4795
Negative (%)50.0%
Memory size75.0 KiB
2022-09-13T13:23:20.473847image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-3.458910737
5-th percentile-1.163087301
Q1-0.4769362807
median0
Q30.4769362807
95-th percentile1.163087301
Maximum3.458910737
Range6.917821474
Interquartile range (IQR)0.9538725615

Descriptive statistics

Standard deviation0.7082351688
Coefficient of variation (CV)-9.558855403 × 1017
Kurtosis0.06957175341
Mean-7.409204752 × 10-19
Median Absolute Deviation (MAD)0.476994291
Skewness8.345166235 × 10-18
Sum2.664535259 × 10-15
Variance0.5015970543
MonotonicityNot monotonic
2022-09-13T13:23:20.625848image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0970690151
 
< 0.1%
0.3170571861
 
< 0.1%
-0.8333086511
 
< 0.1%
-0.0938979121
 
< 0.1%
0.174108971
 
< 0.1%
-0.6347219261
 
< 0.1%
0.2001447621
 
< 0.1%
0.3217652061
 
< 0.1%
1.4428743511
 
< 0.1%
0.3973072671
 
< 0.1%
Other values (9580)9580
99.9%
ValueCountFrequency (%)
-3.4589107371
< 0.1%
-2.6222353681
< 0.1%
-2.4953746651
< 0.1%
-2.4184571121
< 0.1%
-2.3625573651
< 0.1%
-2.318391531
< 0.1%
-2.281756071
< 0.1%
-2.2503797991
< 0.1%
-2.2228925341
< 0.1%
-2.1984023591
< 0.1%
ValueCountFrequency (%)
3.4589107371
< 0.1%
2.6222353681
< 0.1%
2.4953746651
< 0.1%
2.4184571121
< 0.1%
2.3625573651
< 0.1%
2.318391531
< 0.1%
2.281756071
< 0.1%
2.2503797991
< 0.1%
2.2228925341
< 0.1%
2.1984023591
< 0.1%

etherium_before_start
Real number (ℝ≥0)

HIGH CORRELATION

Distinct2505
Distinct (%)26.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean363.2777566
Minimum0.875
Maximum1148
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size75.0 KiB
2022-09-13T13:23:20.854851image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.875
5-th percentile206.7449997
Q1326.2000122
median326.2000122
Q3326.2000122
95-th percentile702.5
Maximum1148
Range1147.125
Interquartile range (IQR)0

Descriptive statistics

Standard deviation147.3337847
Coefficient of variation (CV)0.4055678664
Kurtosis4.141801853
Mean363.2777566
Median Absolute Deviation (MAD)0
Skewness1.288952257
Sum3483833.686
Variance21707.2441
MonotonicityNot monotonic
2022-09-13T13:23:21.033850image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
326.20001226538
68.2%
4215
 
0.1%
435.70001225
 
0.1%
6714
 
< 0.1%
532.59997564
 
< 0.1%
750.79998784
 
< 0.1%
366.20001224
 
< 0.1%
350.79998784
 
< 0.1%
268.54
 
< 0.1%
3344
 
< 0.1%
Other values (2495)3014
31.4%
ValueCountFrequency (%)
0.8751
< 0.1%
1.044999961
< 0.1%
1.317999961
< 0.1%
1.366999861
< 0.1%
1.367999911
< 0.1%
1.427999971
< 0.1%
1.468999981
< 0.1%
1.488999961
< 0.1%
1.493000031
< 0.1%
1.615000011
< 0.1%
ValueCountFrequency (%)
11481
< 0.1%
10531
< 0.1%
10421
< 0.1%
10401
< 0.1%
10261
< 0.1%
10211
< 0.1%
10161
< 0.1%
10122
< 0.1%
10061
< 0.1%
10031
< 0.1%

expected_start
Categorical

HIGH CARDINALITY
UNIFORM

Distinct9070
Distinct (%)95.0%
Missing40
Missing (%)0.4%
Memory size75.0 KiB
10/3/2021 14:12
 
4
8/4/2020 17:47
 
4
7/28/2020 12:52
 
3
7/19/2020 22:29
 
3
2/8/2020 7:27
 
3
Other values (9065)
9533 

Length

Max length16
Median length15
Mean length14.50010471
Min length13

Characters and Unicode

Total characters138476
Distinct characters13
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8599 ?
Unique (%)90.0%

Sample

1st row9/7/2020 13:10
2nd row9/7/2020 15:08
3rd row9/7/2020 16:15
4th row9/7/2020 18:22
5th row9/7/2020 19:14

Common Values

ValueCountFrequency (%)
10/3/2021 14:124
 
< 0.1%
8/4/2020 17:474
 
< 0.1%
7/28/2020 12:523
 
< 0.1%
7/19/2020 22:293
 
< 0.1%
2/8/2020 7:273
 
< 0.1%
7/28/2020 16:043
 
< 0.1%
6/3/2021 18:523
 
< 0.1%
11/14/2019 16:332
 
< 0.1%
12/5/2019 14:352
 
< 0.1%
2/5/2019 1:462
 
< 0.1%
Other values (9060)9521
99.3%
(Missing)40
 
0.4%

Length

2022-09-13T13:23:21.196847image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
7/29/202040
 
0.2%
7/30/202040
 
0.2%
7/28/202037
 
0.2%
7/16/202036
 
0.2%
7/14/202036
 
0.2%
2/8/202033
 
0.2%
7/20/202033
 
0.2%
7/18/202032
 
0.2%
11/7/202032
 
0.2%
6/8/202031
 
0.2%
Other values (2047)18750
98.2%

Most occurring characters

ValueCountFrequency (%)
226128
18.9%
120828
15.0%
019666
14.2%
/19100
13.8%
9550
 
6.9%
:9550
 
6.9%
97051
 
5.1%
36003
 
4.3%
44808
 
3.5%
54785
 
3.5%
Other values (3)11007
7.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number100276
72.4%
Other Punctuation28650
 
20.7%
Space Separator9550
 
6.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
226128
26.1%
120828
20.8%
019666
19.6%
97051
 
7.0%
36003
 
6.0%
44808
 
4.8%
54785
 
4.8%
73973
 
4.0%
63604
 
3.6%
83430
 
3.4%
Other Punctuation
ValueCountFrequency (%)
/19100
66.7%
:9550
33.3%
Space Separator
ValueCountFrequency (%)
9550
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common138476
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
226128
18.9%
120828
15.0%
019666
14.2%
/19100
13.8%
9550
 
6.9%
:9550
 
6.9%
97051
 
5.1%
36003
 
4.3%
44808
 
3.5%
54785
 
3.5%
Other values (3)11007
7.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII138476
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
226128
18.9%
120828
15.0%
019666
14.2%
/19100
13.8%
9550
 
6.9%
:9550
 
6.9%
97051
 
5.1%
36003
 
4.3%
44808
 
3.5%
54785
 
3.5%
Other values (3)11007
7.9%

start_process
Categorical

HIGH CARDINALITY
UNIFORM

Distinct9224
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Memory size75.0 KiB
9/7/2020 13:08
 
2
4/8/2020 6:34
 
2
5/8/2020 6:31
 
2
5/8/2020 4:52
 
2
5/8/2020 3:02
 
2
Other values (9219)
9580 

Length

Max length16
Median length15
Mean length14.49989572
Min length13

Characters and Unicode

Total characters139054
Distinct characters13
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8858 ?
Unique (%)92.4%

Sample

1st row9/7/2020 13:08
2nd row9/7/2020 15:11
3rd row9/7/2020 16:16
4th row9/7/2020 18:24
5th row9/7/2020 19:12

Common Values

ValueCountFrequency (%)
9/7/2020 13:082
 
< 0.1%
4/8/2020 6:342
 
< 0.1%
5/8/2020 6:312
 
< 0.1%
5/8/2020 4:522
 
< 0.1%
5/8/2020 3:022
 
< 0.1%
5/8/2020 1:212
 
< 0.1%
4/8/2020 19:442
 
< 0.1%
4/8/2020 17:582
 
< 0.1%
4/8/2020 16:192
 
< 0.1%
4/8/2020 10:292
 
< 0.1%
Other values (9214)9570
99.8%

Length

2022-09-13T13:23:21.335850image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
7/29/202040
 
0.2%
7/30/202039
 
0.2%
7/28/202037
 
0.2%
7/14/202036
 
0.2%
7/16/202036
 
0.2%
7/20/202034
 
0.2%
2/8/202033
 
0.2%
11/7/202032
 
0.2%
7/18/202032
 
0.2%
6/18/201931
 
0.2%
Other values (2051)18830
98.2%

Most occurring characters

ValueCountFrequency (%)
226089
18.8%
121007
15.1%
019742
14.2%
/19180
13.8%
9590
 
6.9%
:9590
 
6.9%
97148
 
5.1%
36047
 
4.3%
44847
 
3.5%
54824
 
3.5%
Other values (3)10990
7.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number100694
72.4%
Other Punctuation28770
 
20.7%
Space Separator9590
 
6.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
226089
25.9%
121007
20.9%
019742
19.6%
97148
 
7.1%
36047
 
6.0%
44847
 
4.8%
54824
 
4.8%
73856
 
3.8%
63652
 
3.6%
83482
 
3.5%
Other Punctuation
ValueCountFrequency (%)
/19180
66.7%
:9590
33.3%
Space Separator
ValueCountFrequency (%)
9590
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common139054
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
226089
18.8%
121007
15.1%
019742
14.2%
/19180
13.8%
9590
 
6.9%
:9590
 
6.9%
97148
 
5.1%
36047
 
4.3%
44847
 
3.5%
54824
 
3.5%
Other values (3)10990
7.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII139054
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
226089
18.8%
121007
15.1%
019742
14.2%
/19180
13.8%
9590
 
6.9%
:9590
 
6.9%
97148
 
5.1%
36047
 
4.3%
44847
 
3.5%
54824
 
3.5%
Other values (3)10990
7.9%

start_subprocess1
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct9118
Distinct (%)96.2%
Missing113
Missing (%)1.2%
Memory size75.0 KiB
4/8/2020 1:26
 
2
5/8/2020 1:25
 
2
4/8/2020 19:53
 
2
4/8/2020 18:05
 
2
4/8/2020 16:27
 
2
Other values (9113)
9467 

Length

Max length16
Median length15
Mean length14.50005276
Min length13

Characters and Unicode

Total characters137417
Distinct characters13
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8759 ?
Unique (%)92.4%

Sample

1st row9/7/2020 13:11
2nd row9/7/2020 15:16
3rd row9/7/2020 16:20
4th row9/7/2020 18:31
5th row9/7/2020 19:16

Common Values

ValueCountFrequency (%)
4/8/2020 1:262
 
< 0.1%
5/8/2020 1:252
 
< 0.1%
4/8/2020 19:532
 
< 0.1%
4/8/2020 18:052
 
< 0.1%
4/8/2020 16:272
 
< 0.1%
4/8/2020 10:322
 
< 0.1%
4/8/2020 8:352
 
< 0.1%
4/8/2020 6:362
 
< 0.1%
4/8/2020 4:592
 
< 0.1%
4/8/2020 3:252
 
< 0.1%
Other values (9108)9457
98.6%
(Missing)113
 
1.2%

Length

2022-09-13T13:23:21.470882image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
7/29/202040
 
0.2%
7/30/202039
 
0.2%
7/28/202037
 
0.2%
7/14/202036
 
0.2%
7/16/202036
 
0.2%
7/20/202034
 
0.2%
11/7/202032
 
0.2%
7/18/202032
 
0.2%
2/8/202031
 
0.2%
6/8/202031
 
0.2%
Other values (2050)18606
98.2%

Most occurring characters

ValueCountFrequency (%)
225705
18.7%
120704
15.1%
019471
14.2%
/18954
13.8%
9477
 
6.9%
:9477
 
6.9%
97051
 
5.1%
35932
 
4.3%
44895
 
3.6%
54772
 
3.5%
Other values (3)10979
8.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number99509
72.4%
Other Punctuation28431
 
20.7%
Space Separator9477
 
6.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
225705
25.8%
120704
20.8%
019471
19.6%
97051
 
7.1%
35932
 
6.0%
44895
 
4.9%
54772
 
4.8%
73879
 
3.9%
63656
 
3.7%
83444
 
3.5%
Other Punctuation
ValueCountFrequency (%)
/18954
66.7%
:9477
33.3%
Space Separator
ValueCountFrequency (%)
9477
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common137417
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
225705
18.7%
120704
15.1%
019471
14.2%
/18954
13.8%
9477
 
6.9%
:9477
 
6.9%
97051
 
5.1%
35932
 
4.3%
44895
 
3.6%
54772
 
3.5%
Other values (3)10979
8.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII137417
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
225705
18.7%
120704
15.1%
019471
14.2%
/18954
13.8%
9477
 
6.9%
:9477
 
6.9%
97051
 
5.1%
35932
 
4.3%
44895
 
3.6%
54772
 
3.5%
Other values (3)10979
8.0%

start_critical_subprocess1
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct6294
Distinct (%)96.5%
Missing3068
Missing (%)32.0%
Memory size75.0 KiB
9/7/2020 13:13
 
2
6/8/2020 16:32
 
2
4/8/2020 5:04
 
2
4/8/2020 6:41
 
2
4/8/2020 8:41
 
2
Other values (6289)
6512 

Length

Max length16
Median length15
Mean length14.45032199
Min length13

Characters and Unicode

Total characters94245
Distinct characters13
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6066 ?
Unique (%)93.0%

Sample

1st row9/7/2020 13:13
2nd row9/7/2020 15:18
3rd row9/7/2020 16:22
4th row9/7/2020 18:33
5th row9/7/2020 21:04

Common Values

ValueCountFrequency (%)
9/7/2020 13:132
 
< 0.1%
6/8/2020 16:322
 
< 0.1%
4/8/2020 5:042
 
< 0.1%
4/8/2020 6:412
 
< 0.1%
4/8/2020 8:412
 
< 0.1%
4/8/2020 10:392
 
< 0.1%
4/8/2020 16:372
 
< 0.1%
4/8/2020 18:142
 
< 0.1%
4/8/2020 20:002
 
< 0.1%
5/8/2020 6:442
 
< 0.1%
Other values (6284)6502
67.8%
(Missing)3068
32.0%

Length

2022-09-13T13:23:21.609802image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
7/20/202034
 
0.3%
7/28/202033
 
0.3%
5/10/201929
 
0.2%
6/14/201929
 
0.2%
7/13/202028
 
0.2%
4/8/202028
 
0.2%
6/16/201927
 
0.2%
6/23/201927
 
0.2%
8/6/201927
 
0.2%
7/16/202026
 
0.2%
Other values (2012)12756
97.8%

Most occurring characters

ValueCountFrequency (%)
217565
18.6%
113955
14.8%
013387
14.2%
/13044
13.8%
6522
 
6.9%
:6522
 
6.9%
94724
 
5.0%
34095
 
4.3%
53339
 
3.5%
43275
 
3.5%
Other values (3)7817
8.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number68157
72.3%
Other Punctuation19566
 
20.8%
Space Separator6522
 
6.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
217565
25.8%
113955
20.5%
013387
19.6%
94724
 
6.9%
34095
 
6.0%
53339
 
4.9%
43275
 
4.8%
72781
 
4.1%
62625
 
3.9%
82411
 
3.5%
Other Punctuation
ValueCountFrequency (%)
/13044
66.7%
:6522
33.3%
Space Separator
ValueCountFrequency (%)
6522
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common94245
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
217565
18.6%
113955
14.8%
013387
14.2%
/13044
13.8%
6522
 
6.9%
:6522
 
6.9%
94724
 
5.0%
34095
 
4.3%
53339
 
3.5%
43275
 
3.5%
Other values (3)7817
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII94245
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
217565
18.6%
113955
14.8%
013387
14.2%
/13044
13.8%
6522
 
6.9%
:6522
 
6.9%
94724
 
5.0%
34095
 
4.3%
53339
 
3.5%
43275
 
3.5%
Other values (3)7817
8.3%

predicted_process_end
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct8963
Distinct (%)96.1%
Missing267
Missing (%)2.8%
Memory size75.0 KiB
10/3/2021 15:35
 
4
9/7/2020 13:41
 
2
4/8/2020 17:07
 
2
5/8/2020 11:05
 
2
5/8/2020 8:40
 
2
Other values (8958)
9311 

Length

Max length16
Median length15
Mean length14.4993028
Min length13

Characters and Unicode

Total characters135177
Distinct characters13
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8605 ?
Unique (%)92.3%

Sample

1st row9/7/2020 13:41
2nd row9/7/2020 15:49
3rd row9/7/2020 16:54
4th row9/7/2020 19:02
5th row9/7/2020 20:13

Common Values

ValueCountFrequency (%)
10/3/2021 15:354
 
< 0.1%
9/7/2020 13:412
 
< 0.1%
4/8/2020 17:072
 
< 0.1%
5/8/2020 11:052
 
< 0.1%
5/8/2020 8:402
 
< 0.1%
5/8/2020 6:592
 
< 0.1%
5/8/2020 5:202
 
< 0.1%
5/8/2020 3:412
 
< 0.1%
5/8/2020 1:492
 
< 0.1%
4/8/2020 20:512
 
< 0.1%
Other values (8953)9301
97.0%
(Missing)267
 
2.8%

Length

2022-09-13T13:23:21.750835image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
7/30/202041
 
0.2%
7/29/202039
 
0.2%
7/14/202036
 
0.2%
7/20/202035
 
0.2%
7/16/202035
 
0.2%
7/28/202034
 
0.2%
11/7/202032
 
0.2%
2/8/202031
 
0.2%
6/8/202031
 
0.2%
7/18/202031
 
0.2%
Other values (2048)18301
98.1%

Most occurring characters

ValueCountFrequency (%)
225352
18.8%
120353
15.1%
019485
14.4%
/18646
13.8%
9323
 
6.9%
:9323
 
6.9%
96867
 
5.1%
35799
 
4.3%
54842
 
3.6%
44574
 
3.4%
Other values (3)10613
7.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number97885
72.4%
Other Punctuation27969
 
20.7%
Space Separator9323
 
6.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
225352
25.9%
120353
20.8%
019485
19.9%
96867
 
7.0%
35799
 
5.9%
54842
 
4.9%
44574
 
4.7%
73801
 
3.9%
63460
 
3.5%
83352
 
3.4%
Other Punctuation
ValueCountFrequency (%)
/18646
66.7%
:9323
33.3%
Space Separator
ValueCountFrequency (%)
9323
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common135177
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
225352
18.8%
120353
15.1%
019485
14.4%
/18646
13.8%
9323
 
6.9%
:9323
 
6.9%
96867
 
5.1%
35799
 
4.3%
54842
 
3.6%
44574
 
3.4%
Other values (3)10613
7.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII135177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
225352
18.8%
120353
15.1%
019485
14.4%
/18646
13.8%
9323
 
6.9%
:9323
 
6.9%
96867
 
5.1%
35799
 
4.3%
54842
 
3.6%
44574
 
3.4%
Other values (3)10613
7.9%

process_end
Categorical

HIGH CARDINALITY
UNIFORM

Distinct9219
Distinct (%)96.2%
Missing10
Missing (%)0.1%
Memory size75.0 KiB
9/7/2020 13:28
 
2
4/8/2020 7:11
 
2
5/8/2020 5:17
 
2
5/8/2020 3:23
 
2
5/8/2020 1:46
 
2
Other values (9214)
9570 

Length

Max length16
Median length15
Mean length14.50240084
Min length13

Characters and Unicode

Total characters138933
Distinct characters13
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8858 ?
Unique (%)92.5%

Sample

1st row9/7/2020 13:28
2nd row9/7/2020 15:39
3rd row9/7/2020 16:42
4th row9/7/2020 18:47
5th row9/7/2020 19:37

Common Values

ValueCountFrequency (%)
9/7/2020 13:282
 
< 0.1%
4/8/2020 7:112
 
< 0.1%
5/8/2020 5:172
 
< 0.1%
5/8/2020 3:232
 
< 0.1%
5/8/2020 1:462
 
< 0.1%
4/8/2020 20:432
 
< 0.1%
4/8/2020 18:552
 
< 0.1%
4/8/2020 17:072
 
< 0.1%
4/8/2020 11:082
 
< 0.1%
4/8/2020 9:162
 
< 0.1%
Other values (9209)9560
99.7%
(Missing)10
 
0.1%

Length

2022-09-13T13:23:21.873838image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
7/30/202041
 
0.2%
7/29/202038
 
0.2%
7/28/202038
 
0.2%
7/16/202036
 
0.2%
7/14/202036
 
0.2%
7/20/202035
 
0.2%
2/8/202033
 
0.2%
11/7/202032
 
0.2%
10/7/202031
 
0.2%
7/18/202031
 
0.2%
Other values (2052)18809
98.2%

Most occurring characters

ValueCountFrequency (%)
226078
18.8%
120872
15.0%
019833
14.3%
/19160
13.8%
9580
 
6.9%
:9580
 
6.9%
97105
 
5.1%
35999
 
4.3%
54910
 
3.5%
44712
 
3.4%
Other values (3)11104
8.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number100613
72.4%
Other Punctuation28740
 
20.7%
Space Separator9580
 
6.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
226078
25.9%
120872
20.7%
019833
19.7%
97105
 
7.1%
35999
 
6.0%
54910
 
4.9%
44712
 
4.7%
74031
 
4.0%
63586
 
3.6%
83487
 
3.5%
Other Punctuation
ValueCountFrequency (%)
/19160
66.7%
:9580
33.3%
Space Separator
ValueCountFrequency (%)
9580
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common138933
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
226078
18.8%
120872
15.0%
019833
14.3%
/19160
13.8%
9580
 
6.9%
:9580
 
6.9%
97105
 
5.1%
35999
 
4.3%
54910
 
3.5%
44712
 
3.4%
Other values (3)11104
8.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII138933
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
226078
18.8%
120872
15.0%
019833
14.3%
/19160
13.8%
9580
 
6.9%
:9580
 
6.9%
97105
 
5.1%
35999
 
4.3%
54910
 
3.5%
44712
 
3.4%
Other values (3)11104
8.0%

subprocess1_end
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct9113
Distinct (%)96.2%
Missing120
Missing (%)1.3%
Memory size75.0 KiB
9/7/2020 13:27
 
2
4/8/2020 20:43
 
2
4/8/2020 17:05
 
2
4/8/2020 11:08
 
2
4/8/2020 9:15
 
2
Other values (9108)
9460 

Length

Max length16
Median length15
Mean length14.50168955
Min length13

Characters and Unicode

Total characters137331
Distinct characters13
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8756 ?
Unique (%)92.5%

Sample

1st row9/7/2020 13:27
2nd row9/7/2020 15:38
3rd row9/7/2020 16:41
4th row9/7/2020 18:47
5th row9/7/2020 19:37

Common Values

ValueCountFrequency (%)
9/7/2020 13:272
 
< 0.1%
4/8/2020 20:432
 
< 0.1%
4/8/2020 17:052
 
< 0.1%
4/8/2020 11:082
 
< 0.1%
4/8/2020 9:152
 
< 0.1%
4/8/2020 7:082
 
< 0.1%
4/8/2020 5:322
 
< 0.1%
4/8/2020 3:522
 
< 0.1%
4/8/2020 2:222
 
< 0.1%
3/8/2020 21:512
 
< 0.1%
Other values (9103)9450
98.5%
(Missing)120
 
1.3%

Length

2022-09-13T13:23:22.004549image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
7/30/202041
 
0.2%
7/28/202038
 
0.2%
7/29/202038
 
0.2%
7/14/202036
 
0.2%
7/16/202036
 
0.2%
7/20/202034
 
0.2%
11/7/202032
 
0.2%
7/18/202031
 
0.2%
6/8/202031
 
0.2%
2/8/202031
 
0.2%
Other values (2049)18592
98.2%

Most occurring characters

ValueCountFrequency (%)
225878
18.8%
120644
15.0%
019491
14.2%
/18940
13.8%
9470
 
6.9%
:9470
 
6.9%
96970
 
5.1%
35848
 
4.3%
54817
 
3.5%
44710
 
3.4%
Other values (3)11093
8.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number99451
72.4%
Other Punctuation28410
 
20.7%
Space Separator9470
 
6.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
225878
26.0%
120644
20.8%
019491
19.6%
96970
 
7.0%
35848
 
5.9%
54817
 
4.8%
44710
 
4.7%
74043
 
4.1%
63615
 
3.6%
83435
 
3.5%
Other Punctuation
ValueCountFrequency (%)
/18940
66.7%
:9470
33.3%
Space Separator
ValueCountFrequency (%)
9470
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common137331
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
225878
18.8%
120644
15.0%
019491
14.2%
/18940
13.8%
9470
 
6.9%
:9470
 
6.9%
96970
 
5.1%
35848
 
4.3%
54817
 
3.5%
44710
 
3.4%
Other values (3)11093
8.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII137331
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
225878
18.8%
120644
15.0%
019491
14.2%
/18940
13.8%
9470
 
6.9%
:9470
 
6.9%
96970
 
5.1%
35848
 
4.3%
54817
 
3.5%
44710
 
3.4%
Other values (3)11093
8.1%

reported_on_tower
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct8883
Distinct (%)94.6%
Missing203
Missing (%)2.1%
Memory size75.0 KiB
9/11/2019 23:57
 
4
10/1/2020 12:53
 
3
12/15/2019 0:20
 
3
6/4/2020 14:28
 
3
7/24/2020 19:13
 
3
Other values (8878)
9371 

Length

Max length16
Median length15
Mean length14.50143816
Min length13

Characters and Unicode

Total characters136125
Distinct characters13
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8396 ?
Unique (%)89.4%

Sample

1st row9/7/2020 13:37
2nd row9/7/2020 15:53
3rd row9/7/2020 16:54
4th row9/7/2020 18:55
5th row9/7/2020 19:47

Common Values

ValueCountFrequency (%)
9/11/2019 23:574
 
< 0.1%
10/1/2020 12:533
 
< 0.1%
12/15/2019 0:203
 
< 0.1%
6/4/2020 14:283
 
< 0.1%
7/24/2020 19:133
 
< 0.1%
6/29/2019 23:173
 
< 0.1%
7/20/2020 0:293
 
< 0.1%
6/1/2021 10:163
 
< 0.1%
7/28/2020 17:283
 
< 0.1%
10/1/2020 11:463
 
< 0.1%
Other values (8873)9356
97.6%
(Missing)203
 
2.1%

Length

2022-09-13T13:23:22.136581image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
7/30/202041
 
0.2%
7/28/202038
 
0.2%
7/29/202036
 
0.2%
7/14/202036
 
0.2%
7/16/202035
 
0.2%
7/20/202034
 
0.2%
11/7/202033
 
0.2%
7/18/202032
 
0.2%
1/8/202031
 
0.2%
2/8/202031
 
0.2%
Other values (2051)18427
98.2%

Most occurring characters

ValueCountFrequency (%)
225561
18.8%
120536
15.1%
019308
14.2%
/18774
13.8%
9387
 
6.9%
:9387
 
6.9%
96911
 
5.1%
35855
 
4.3%
54802
 
3.5%
44683
 
3.4%
Other values (3)10921
8.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number98577
72.4%
Other Punctuation28161
 
20.7%
Space Separator9387
 
6.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
225561
25.9%
120536
20.8%
019308
19.6%
96911
 
7.0%
35855
 
5.9%
54802
 
4.9%
44683
 
4.8%
73985
 
4.0%
63557
 
3.6%
83379
 
3.4%
Other Punctuation
ValueCountFrequency (%)
/18774
66.7%
:9387
33.3%
Space Separator
ValueCountFrequency (%)
9387
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common136125
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
225561
18.8%
120536
15.1%
019308
14.2%
/18774
13.8%
9387
 
6.9%
:9387
 
6.9%
96911
 
5.1%
35855
 
4.3%
54802
 
3.5%
44683
 
3.4%
Other values (3)10921
8.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII136125
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
225561
18.8%
120536
15.1%
019308
14.2%
/18774
13.8%
9387
 
6.9%
:9387
 
6.9%
96911
 
5.1%
35855
 
4.3%
54802
 
3.5%
44683
 
3.4%
Other values (3)10921
8.0%

opened
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct8914
Distinct (%)95.0%
Missing203
Missing (%)2.1%
Memory size75.0 KiB
15/07/2020 17:39
 
3
19/03/2020 02:14
 
3
30/01/2021 10:49
 
3
29/06/2019 23:27
 
3
10/1/2020 13:04
 
3
Other values (8909)
9372 

Length

Max length16
Median length16
Mean length15.07254714
Min length9

Characters and Unicode

Total characters141486
Distinct characters14
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8456 ?
Unique (%)90.1%

Sample

1st row44021.58091
2nd row44021.6737
3rd row44021.70867
4th row9/7/2020 19:02
5th row9/7/2020 20:20

Common Values

ValueCountFrequency (%)
15/07/2020 17:393
 
< 0.1%
19/03/2020 02:143
 
< 0.1%
30/01/2021 10:493
 
< 0.1%
29/06/2019 23:273
 
< 0.1%
10/1/2020 13:043
 
< 0.1%
6/4/2020 15:033
 
< 0.1%
24/07/2020 20:013
 
< 0.1%
9/8/2020 12:003
 
< 0.1%
6/1/2021 10:223
 
< 0.1%
10/1/2020 11:563
 
< 0.1%
Other values (8904)9357
97.6%
(Missing)203
 
2.1%

Length

2022-09-13T13:23:22.263548image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
30/07/202040
 
0.2%
14/07/202038
 
0.2%
28/07/202038
 
0.2%
29/07/202036
 
0.2%
16/07/202035
 
0.2%
11/7/202033
 
0.2%
20/07/202032
 
0.2%
10/7/202032
 
0.2%
2/8/202031
 
0.2%
6/8/202031
 
0.2%
Other values (2793)18210
98.1%

Most occurring characters

ValueCountFrequency (%)
025292
17.9%
225142
17.8%
120278
14.3%
/18338
13.0%
9169
 
6.5%
:9169
 
6.5%
97018
 
5.0%
35941
 
4.2%
45000
 
3.5%
54810
 
3.4%
Other values (4)11329
8.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number104592
73.9%
Other Punctuation27725
 
19.6%
Space Separator9169
 
6.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
025292
24.2%
225142
24.0%
120278
19.4%
97018
 
6.7%
35941
 
5.7%
45000
 
4.8%
54810
 
4.6%
73980
 
3.8%
63659
 
3.5%
83472
 
3.3%
Other Punctuation
ValueCountFrequency (%)
/18338
66.1%
:9169
33.1%
.218
 
0.8%
Space Separator
ValueCountFrequency (%)
9169
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common141486
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
025292
17.9%
225142
17.8%
120278
14.3%
/18338
13.0%
9169
 
6.5%
:9169
 
6.5%
97018
 
5.0%
35941
 
4.2%
45000
 
3.5%
54810
 
3.4%
Other values (4)11329
8.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII141486
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
025292
17.9%
225142
17.8%
120278
14.3%
/18338
13.0%
9169
 
6.5%
:9169
 
6.5%
97018
 
5.0%
35941
 
4.2%
45000
 
3.5%
54810
 
3.4%
Other values (4)11329
8.0%

chemical_x
Real number (ℝ≥0)

HIGH CORRELATION

Distinct2837
Distinct (%)29.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.10466979
Minimum0.45
Maximum104.7666667
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size75.0 KiB
2022-09-13T13:23:22.401551image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.45
5-th percentile13.5575
Q120.4
median27.88333333
Q337.39583333
95-th percentile53.71666667
Maximum104.7666667
Range104.3166667
Interquartile range (IQR)16.99583333

Descriptive statistics

Standard deviation12.89495566
Coefficient of variation (CV)0.4283373891
Kurtosis1.518581834
Mean30.10466979
Median Absolute Deviation (MAD)8.3
Skewness1.01406056
Sum288703.7833
Variance166.2798815
MonotonicityNot monotonic
2022-09-13T13:23:22.588552image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27.88333333122
 
1.3%
2114
 
0.1%
19.8166666714
 
0.1%
23.513
 
0.1%
17.1166666713
 
0.1%
22.9513
 
0.1%
34.1833333313
 
0.1%
1913
 
0.1%
15.2512
 
0.1%
23.3333333312
 
0.1%
Other values (2827)9351
97.5%
ValueCountFrequency (%)
0.451
< 0.1%
1.283333331
< 0.1%
1.351
< 0.1%
1.566666671
< 0.1%
2.016666671
< 0.1%
2.133333331
< 0.1%
2.751
< 0.1%
2.91
< 0.1%
2.916666671
< 0.1%
2.951
< 0.1%
ValueCountFrequency (%)
104.76666671
< 0.1%
104.33333331
< 0.1%
103.91
< 0.1%
101.81666671
< 0.1%
99.533333331
< 0.1%
98.183333331
< 0.1%
92.21
< 0.1%
91.983333331
< 0.1%
91.716666671
< 0.1%
91.183333331
< 0.1%

raw_kryptonite
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct9590
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0
Minimum-3.458910737
Maximum3.458910737
Zeros0
Zeros (%)0.0%
Negative4795
Negative (%)50.0%
Memory size75.0 KiB
2022-09-13T13:23:22.758548image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-3.458910737
5-th percentile-1.163087301
Q1-0.4769362807
median0
Q30.4769362807
95-th percentile1.163087301
Maximum3.458910737
Range6.917821474
Interquartile range (IQR)0.9538725615

Descriptive statistics

Standard deviation0.7082351688
Coefficient of variation (CV)nan
Kurtosis0.06957175341
Mean0
Median Absolute Deviation (MAD)0.476994291
Skewness4.172583118 × 10-18
Sum3.985700658 × 10-14
Variance0.5015970543
MonotonicityNot monotonic
2022-09-13T13:23:22.902558image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.9381643711
 
< 0.1%
-0.1982215291
 
< 0.1%
-0.1984137861
 
< 0.1%
0.4809468981
 
< 0.1%
0.640829621
 
< 0.1%
-0.6397155821
 
< 0.1%
-0.7515798971
 
< 0.1%
-0.7597596351
 
< 0.1%
1.1706246861
 
< 0.1%
-0.5579506571
 
< 0.1%
Other values (9580)9580
99.9%
ValueCountFrequency (%)
-3.4589107371
< 0.1%
-2.6222353681
< 0.1%
-2.4953746651
< 0.1%
-2.4184571121
< 0.1%
-2.3625573651
< 0.1%
-2.318391531
< 0.1%
-2.281756071
< 0.1%
-2.2503797991
< 0.1%
-2.2228925341
< 0.1%
-2.1984023591
< 0.1%
ValueCountFrequency (%)
3.4589107371
< 0.1%
2.6222353681
< 0.1%
2.4953746651
< 0.1%
2.4184571121
< 0.1%
2.3625573651
< 0.1%
2.318391531
< 0.1%
2.281756071
< 0.1%
2.2503797991
< 0.1%
2.2228925341
< 0.1%
2.1984023591
< 0.1%

argon
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct9590
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-5.927363802 × 10-18
Minimum-3.458910737
Maximum3.458910737
Zeros0
Zeros (%)0.0%
Negative4795
Negative (%)50.0%
Memory size75.0 KiB
2022-09-13T13:23:23.067549image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-3.458910737
5-th percentile-1.163087301
Q1-0.4769362807
median0
Q30.4769362807
95-th percentile1.163087301
Maximum3.458910737
Range6.917821474
Interquartile range (IQR)0.9538725615

Descriptive statistics

Standard deviation0.7082351688
Coefficient of variation (CV)-1.194856925 × 1017
Kurtosis0.06957175341
Mean-5.927363802 × 10-18
Median Absolute Deviation (MAD)0.476994291
Skewness6.676132988 × 10-17
Sum-9.958700531 × 10-14
Variance0.5015970543
MonotonicityNot monotonic
2022-09-13T13:23:23.228547image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-3.4589107371
 
< 0.1%
-1.1390632191
 
< 0.1%
0.7873324021
 
< 0.1%
0.2746838771
 
< 0.1%
0.1503863081
 
< 0.1%
0.5479127911
 
< 0.1%
0.5389722181
 
< 0.1%
0.2221784571
 
< 0.1%
0.1287106071
 
< 0.1%
-1.1397402651
 
< 0.1%
Other values (9580)9580
99.9%
ValueCountFrequency (%)
-3.4589107371
< 0.1%
-2.6222353681
< 0.1%
-2.4953746651
< 0.1%
-2.4184571121
< 0.1%
-2.3625573651
< 0.1%
-2.318391531
< 0.1%
-2.281756071
< 0.1%
-2.2503797991
< 0.1%
-2.2228925341
< 0.1%
-2.1984023591
< 0.1%
ValueCountFrequency (%)
3.4589107371
< 0.1%
2.6222353681
< 0.1%
2.4953746651
< 0.1%
2.4184571121
< 0.1%
2.3625573651
< 0.1%
2.318391531
< 0.1%
2.281756071
< 0.1%
2.2503797991
< 0.1%
2.2228925341
< 0.1%
2.1984023591
< 0.1%

pure_seastone
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct9590
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.963681901 × 10-18
Minimum-3.458910737
Maximum3.458910737
Zeros0
Zeros (%)0.0%
Negative4795
Negative (%)50.0%
Memory size75.0 KiB
2022-09-13T13:23:23.396548image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-3.458910737
5-th percentile-1.163087301
Q1-0.4769362807
median0
Q30.4769362807
95-th percentile1.163087301
Maximum3.458910737
Range6.917821474
Interquartile range (IQR)0.9538725615

Descriptive statistics

Standard deviation0.7082351688
Coefficient of variation (CV)2.389713851 × 1017
Kurtosis0.06957175341
Mean2.963681901 × 10-18
Median Absolute Deviation (MAD)0.476994291
Skewness-1.669033247 × 10-17
Sum1.998401444 × 10-15
Variance0.5015970543
MonotonicityNot monotonic
2022-09-13T13:23:23.732572image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.949425591
 
< 0.1%
0.4769942911
 
< 0.1%
0.9457958711
 
< 0.1%
0.8891873961
 
< 0.1%
0.5709144871
 
< 0.1%
0.829618511
 
< 0.1%
0.8248548441
 
< 0.1%
0.5966451831
 
< 0.1%
-0.8355336721
 
< 0.1%
-0.6022050931
 
< 0.1%
Other values (9580)9580
99.9%
ValueCountFrequency (%)
-3.4589107371
< 0.1%
-2.6222353681
< 0.1%
-2.4953746651
< 0.1%
-2.4184571121
< 0.1%
-2.3625573651
< 0.1%
-2.318391531
< 0.1%
-2.281756071
< 0.1%
-2.2503797991
< 0.1%
-2.2228925341
< 0.1%
-2.1984023591
< 0.1%
ValueCountFrequency (%)
3.4589107371
< 0.1%
2.6222353681
< 0.1%
2.4953746651
< 0.1%
2.4184571121
< 0.1%
2.3625573651
< 0.1%
2.318391531
< 0.1%
2.281756071
< 0.1%
2.2503797991
< 0.1%
2.2228925341
< 0.1%
2.1984023591
< 0.1%

crystal_supergroup
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size75.0 KiB
0
7553 
1
2037 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9590
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
07553
78.8%
12037
 
21.2%

Length

2022-09-13T13:23:23.855246image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-13T13:23:23.946407image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
07553
78.8%
12037
 
21.2%

Most occurring characters

ValueCountFrequency (%)
07553
78.8%
12037
 
21.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number9590
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
07553
78.8%
12037
 
21.2%

Most occurring scripts

ValueCountFrequency (%)
Common9590
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
07553
78.8%
12037
 
21.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII9590
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
07553
78.8%
12037
 
21.2%

Cycle
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size75.0 KiB
4977 
4353 
 
134
131
 
126

Length

Max length3
Median length2
Mean length2.013138686
Min length2

Characters and Unicode

Total characters19306
Distinct characters4
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
4977
51.9%
4353
45.4%
134
 
1.4%
131126
 
1.3%

Length

2022-09-13T13:23:24.031376image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-13T13:23:24.138408image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
4977
51.9%
4353
45.4%
134
 
1.4%
131126
 
1.3%

Most occurring characters

ValueCountFrequency (%)
ª9464
49.0%
15229
27.1%
24353
22.5%
3260
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number9842
51.0%
Other Letter9464
49.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
15229
53.1%
24353
44.2%
3260
 
2.6%
Other Letter
ValueCountFrequency (%)
ª9464
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common9842
51.0%
Latin9464
49.0%

Most frequent character per script

Common
ValueCountFrequency (%)
15229
53.1%
24353
44.2%
3260
 
2.6%
Latin
ValueCountFrequency (%)
ª9464
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII9842
51.0%
None9464
49.0%

Most frequent character per block

None
ValueCountFrequency (%)
ª9464
100.0%
ASCII
ValueCountFrequency (%)
15229
53.1%
24353
44.2%
3260
 
2.6%

groups
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct75
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.27841502
Minimum0
Maximum77
Zeros154
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size75.0 KiB
2022-09-13T13:23:24.247370image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q118
median37
Q356
95-th percentile72
Maximum77
Range77
Interquartile range (IQR)38

Descriptive statistics

Standard deviation22.01164055
Coefficient of variation (CV)0.5904661058
Kurtosis-1.157097059
Mean37.27841502
Median Absolute Deviation (MAD)19
Skewness0.0345285849
Sum357500
Variance484.5123195
MonotonicityNot monotonic
2022-09-13T13:23:24.381371image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14171
 
1.8%
63167
 
1.7%
62155
 
1.6%
48155
 
1.6%
0154
 
1.6%
61152
 
1.6%
41151
 
1.6%
12149
 
1.6%
1149
 
1.6%
75149
 
1.6%
Other values (65)8038
83.8%
ValueCountFrequency (%)
0154
1.6%
1149
1.6%
2144
1.5%
381
0.8%
4103
1.1%
5114
1.2%
6112
1.2%
7128
1.3%
8134
1.4%
9133
1.4%
ValueCountFrequency (%)
77136
1.4%
75149
1.6%
74141
1.5%
72135
1.4%
71144
1.5%
70132
1.4%
69128
1.3%
67109
1.1%
66114
1.2%
6591
0.9%

target
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct7110
Distinct (%)83.2%
Missing1045
Missing (%)10.9%
Infinite0
Infinite (%)0.0%
Mean93.0034415
Minimum0
Maximum265
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size75.0 KiB
2022-09-13T13:23:24.524701image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9.683541323
Q145.20833333
median89.80645161
Q3135.8208955
95-th percentile189.5427811
Maximum265
Range265
Interquartile range (IQR)90.61256217

Descriptive statistics

Standard deviation56.74600946
Coefficient of variation (CV)0.6101495659
Kurtosis-0.7323809909
Mean93.0034415
Median Absolute Deviation (MAD)45.19652253
Skewness0.300147952
Sum794714.4076
Variance3220.10959
MonotonicityNot monotonic
2022-09-13T13:23:24.661703image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10012
 
0.1%
14011
 
0.1%
113.33333339
 
0.1%
1209
 
0.1%
56.666666678
 
0.1%
808
 
0.1%
1708
 
0.1%
1508
 
0.1%
858
 
0.1%
1108
 
0.1%
Other values (7100)8456
88.2%
(Missing)1045
 
10.9%
ValueCountFrequency (%)
01
< 0.1%
1.0447761191
< 0.1%
1.0791366911
< 0.1%
1.0967741941
< 0.1%
1.1258278151
< 0.1%
1.1409395971
< 0.1%
1.1564625851
< 0.1%
1.190476191
< 0.1%
1.2056737591
< 0.1%
1.2280701751
< 0.1%
ValueCountFrequency (%)
2651
< 0.1%
263.0370371
< 0.1%
261.07407411
< 0.1%
2601
< 0.1%
259.11111111
< 0.1%
257.47572821
< 0.1%
257.14814811
< 0.1%
255.18518521
< 0.1%
254.95145631
< 0.1%
253.22222221
< 0.1%

Interactions

2022-09-13T13:23:08.829711image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-13T13:22:23.460427image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-13T13:22:25.924593image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-13T13:22:28.465460image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-13T13:22:30.821096image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-13T13:22:33.313555image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-13T13:22:35.815996image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-13T13:22:38.108576image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-13T13:22:40.576320image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-13T13:22:43.043255image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-13T13:22:45.811085image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-13T13:22:48.136492image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-13T13:22:50.967185image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-13T13:22:53.402550image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-13T13:22:56.123228image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-13T13:22:58.718964image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-13T13:23:01.059768image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-13T13:23:03.557392image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-13T13:23:06.220054image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-13T13:23:08.957710image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-13T13:22:23.739470image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-13T13:22:26.044592image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-13T13:22:28.588473image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-13T13:22:30.940098image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-13T13:22:33.447561image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-13T13:22:35.938995image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-13T13:22:38.228605image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-13T13:22:40.708358image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-13T13:22:43.162261image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-13T13:22:45.935086image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-13T13:22:48.477530image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-13T13:22:51.081777image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-13T13:22:53.559544image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-13T13:22:56.248225image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-13T13:22:58.861964image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-13T13:23:01.176299image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-13T13:23:03.668411image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-13T13:23:06.397055image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-13T13:23:09.065708image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-13T13:22:23.873461image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-13T13:22:26.164594image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-13T13:22:28.713463image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-13T13:22:31.057095image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-13T13:22:33.593561image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-13T13:22:36.058027image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-13T13:22:38.346605image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-13T13:22:40.842321image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-13T13:22:43.280229image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-13T13:22:46.052084image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-13T13:22:48.602492image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-13T13:22:51.193817image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-13T13:22:53.756545image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-13T13:22:56.368267image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-13T13:22:58.989964image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-13T13:23:01.483721image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-13T13:23:03.779428image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-13T13:23:06.573309image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-13T13:23:09.225711image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-13T13:22:23.992464image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-13T13:22:26.287593image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-13T13:22:28.856461image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-13T13:22:31.180104image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-13T13:22:33.722602image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-13T13:22:36.179005image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-13T13:22:38.472570image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-13T13:22:40.970323image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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Correlations

2022-09-13T13:23:24.810451image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-09-13T13:23:25.174415image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-09-13T13:23:25.532446image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-09-13T13:23:25.836417image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-09-13T13:23:26.004411image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

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A simple visualization of nullity by column.
2022-09-13T13:23:14.143165image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
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The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-09-13T13:23:14.861296image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

df_indexwhensuper_hero_grouptrackingplacetracking_timescrystal_typeUnnamed_7human_behavior_reporthuman_measurecrystal_weightexpected_factor_xprevious_factor_xfirst_factor_xexpected_final_factor_xfinal_factor_xprevious_adamantiumUnnamed_17etherium_before_startexpected_startstart_processstart_subprocess1start_critical_subprocess1predicted_process_endprocess_endsubprocess1_endreported_on_toweropenedchemical_xraw_kryptoniteargonpure_seastonecrystal_supergroupCyclegroupstarget
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119/7/2020D8494111group 56140.3115480.693627-0.7801580.3399130.021907-0.607001-0.576824-0.4191440.021907326.2000129/7/2020 15:089/7/2020 15:119/7/2020 15:169/7/2020 15:189/7/2020 15:499/7/2020 15:399/7/2020 15:389/7/2020 15:5344021.673721.9666670.856328-0.007671-0.618571001.233766
229/7/2020D8495111group 56240.8636860.429101-0.8448950.6489540.557194-0.606199-0.720132-0.0191340.557194326.2000129/7/2020 16:159/7/2020 16:169/7/2020 16:209/7/2020 16:229/7/2020 16:549/7/2020 16:429/7/2020 16:419/7/2020 16:5444021.7086721.1666671.741829-0.008595-0.654892002.467532
339/7/2020D8497111group 56730.324843-0.688264-0.277277-0.262169-0.293117-0.378400-0.3570280.047448-0.293117326.2000129/7/2020 18:229/7/2020 18:249/7/2020 18:319/7/2020 18:339/7/2020 19:029/7/2020 18:479/7/2020 18:479/7/2020 18:559/7/2020 19:0215.250000-0.858255-0.0100740.088306003.701299
449/7/2020D8498111group 271730.3256651.536116-0.351788-0.1084640.1926520.0606100.5576980.5551790.192652326.2000129/7/2020 19:149/7/2020 19:129/7/2020 19:16NaN9/7/2020 20:139/7/2020 19:379/7/2020 19:379/7/2020 19:479/7/2020 20:2020.5666670.134729-0.0119230.420467004.935065
559/7/2020B8499111group 561230.4943110.6270160.1277710.7336130.9120660.3914770.482112-0.1844170.912066326.2000129/7/2020 20:599/7/2020 20:599/7/2020 21:039/7/2020 21:049/7/2020 21:379/7/2020 21:359/7/2020 21:359/7/2020 21:519/7/2020 22:0932.016667-0.722308-0.012108-0.495256006.168831
669/7/2020B8500111group 5614-1.0564740.760748-0.5612370.4057770.273488-0.588243-0.528649-0.1277710.273488326.2000129/7/2020 21:589/7/2020 22:039/7/2020 22:129/7/2020 22:159/7/2020 22:419/7/2020 22:349/7/2020 22:339/7/2020 22:449/7/2020 23:0321.0333330.924957-0.009705-0.859414007.402597
779/7/2020B8501111group 561740.0254211.778680-0.889595-0.583553-0.149441-0.532568-0.480248-0.053748-0.149441326.2000129/7/2020 23:029/7/2020 23:029/7/2020 23:079/7/2020 23:139/7/2020 23:409/7/2020 23:249/7/2020 23:239/7/2020 23:3410/7/2020 0:0415.8500000.512651-0.0063770.089052008.636364
889/7/2020B8502111group 5624-0.6469861.341668-0.3283390.1836520.292916-0.502605-0.3820310.1275830.292916326.2000129/7/2020 23:519/7/2020 23:519/7/2020 23:549/7/2020 23:5610/7/2020 0:3310/7/2020 0:1610/7/2020 0:1610/7/2020 0:2810/7/2020 1:0721.8333330.860189-0.0012010.089424009.870130
9910/7/2020B8503111group 5673-1.4674480.942191-0.1604230.3901850.747366-0.309716-0.0032350.0598680.747366326.20001210/7/2020 1:1210/7/2020 1:0910/7/2020 1:1110/7/2020 1:1310/7/2020 1:5010/7/2020 1:3110/7/2020 1:2810/7/2020 1:4110/7/2020 2:0816.4833330.918044-0.0017560.0877480011.103896

Last rows

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